Combining strategies for the estimation of treatment effects

Detalhes bibliográficos
Autor(a) principal: Firpo, Sergio
Data de Publicação: 2012
Outros Autores: Pinto, Rafael de Carvalho Cayres
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Biblioteca Digital do Banco Nacional de Desenvolvimento Econômico e Social
Texto Completo: http://web.bndes.gov.br/bib/jspui/handle/1408/10614
Resumo: Bibliografia: p. 68-71.
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spelling Firpo, SergioPinto, Rafael de Carvalho Cayres2017-01-03T12:58:34Z2018-03-19T17:57:04Z2017-01-03T12:58:34Z2018-03-19T17:57:04Z2012-05FIRPO, Sergio; PINTO, Rafael de Carvalho Cayres. Combining strategies for the estimation of treatment effects. Brazilian Review of Econometrics, Rio de Janeiro, v. 32, n. 1, p. 31-71, maio 2012.http://web.bndes.gov.br/bib/jspui/handle/1408/10614Bibliografia: p. 68-71.The estimation of the average effect of a program or treatment on a variable of interest is an important tool for the assessment of economic policies. In general, assignment of potential participants to treatment does not occur at random and could thus generate a selection bias in absence of some correction. A way to get around this problem is by assuming that the econometrician observes a set of determinant characteristics of participation up to a strictly random component. Under such an assumption, the literature contains semiparametric estimators of the average treatment effect that are consistente and can asymptotically reach the semiparametric effciency bound. However, in frequently available samples, the performance of these methods is not always satisfactory. The aim of this paper is to investigate how the combination of two strategies may generate estimators with better properties in small samples. Therefore, we consider two ways of combining these approaches, based on the double robustness literature developed by James Robins et al. We analyze the properties of these combined estimators and discuss why they can outperform the separate use of each method. Finally, using a Monte Carlo simulation, we compare the performance of these estimators with that of the imputation and reweighting techniques. Our results show that the combination of strategies can reduce bias and variance, but this improvement depends on adequate implementation. We conclude that the choice of smoothing parameters is decisive for the performance of estimators in medium-sized samples.p. 31-71Sociedade Brasileira de EconometriaModelos econométricosEconometric modelsMonte Carlo, Método deMonte Carlo methodAnálise de regressãoRegression analysisCombining strategies for the estimation of treatment effectsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article5TextualProdução BNDESRio de Janeiroengreponame:Biblioteca Digital do Banco Nacional de Desenvolvimento Econômico e Socialinstname:Banco Nacional de Desenvolvimento Econômico e Social (BNDES)instacron:BNDESinfo:eu-repo/semantics/openAccessORIGINALCombining strategies for the estimation of treatment effects_Rafael Cayres_P_BD.pdfapplication/pdf441828http://web.bndes.gov.br/bib/jspui/bitstream/1408/10614/1/Combining%20strategies%20for%20the%20estimation%20of%20treatment%20effects_Rafael%20Cayres_P_BD.pdfd389693162224c23a263995d4508e55cMD51LICENSElicense.txttext/plain407http://web.bndes.gov.br/bib/jspui/bitstream/1408/10614/2/license.txtaeb6b64eb9f816a596cef5045906f205MD521408/106142023-04-19 17:10:05.943oai:web.bndes.gov.br: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Repositório Temáticohttps://web.bndes.gov.br/bib/jspui/http://web.bndes.gov.br/bib/oai/requestopendoar:2023-04-19T17:10:05Biblioteca Digital do Banco Nacional de Desenvolvimento Econômico e Social - Banco Nacional de Desenvolvimento Econômico e Social (BNDES)false
dc.title.pt_BR.fl_str_mv Combining strategies for the estimation of treatment effects
title Combining strategies for the estimation of treatment effects
spellingShingle Combining strategies for the estimation of treatment effects
Firpo, Sergio
Modelos econométricos
Econometric models
Monte Carlo, Método de
Monte Carlo method
Análise de regressão
Regression analysis
title_short Combining strategies for the estimation of treatment effects
title_full Combining strategies for the estimation of treatment effects
title_fullStr Combining strategies for the estimation of treatment effects
title_full_unstemmed Combining strategies for the estimation of treatment effects
title_sort Combining strategies for the estimation of treatment effects
author Firpo, Sergio
author_facet Firpo, Sergio
Pinto, Rafael de Carvalho Cayres
author_role author
author2 Pinto, Rafael de Carvalho Cayres
author2_role author
dc.contributor.author.fl_str_mv Firpo, Sergio
Pinto, Rafael de Carvalho Cayres
dc.subject.por.fl_str_mv Modelos econométricos
Econometric models
Monte Carlo, Método de
Monte Carlo method
Análise de regressão
Regression analysis
topic Modelos econométricos
Econometric models
Monte Carlo, Método de
Monte Carlo method
Análise de regressão
Regression analysis
description Bibliografia: p. 68-71.
publishDate 2012
dc.date.issued.fl_str_mv 2012-05
dc.date.accessioned.fl_str_mv 2017-01-03T12:58:34Z
2018-03-19T17:57:04Z
dc.date.available.fl_str_mv 2017-01-03T12:58:34Z
2018-03-19T17:57:04Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.citation.fl_str_mv FIRPO, Sergio; PINTO, Rafael de Carvalho Cayres. Combining strategies for the estimation of treatment effects. Brazilian Review of Econometrics, Rio de Janeiro, v. 32, n. 1, p. 31-71, maio 2012.
dc.identifier.uri.fl_str_mv http://web.bndes.gov.br/bib/jspui/handle/1408/10614
identifier_str_mv FIRPO, Sergio; PINTO, Rafael de Carvalho Cayres. Combining strategies for the estimation of treatment effects. Brazilian Review of Econometrics, Rio de Janeiro, v. 32, n. 1, p. 31-71, maio 2012.
url http://web.bndes.gov.br/bib/jspui/handle/1408/10614
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv p. 31-71
dc.publisher.none.fl_str_mv Sociedade Brasileira de Econometria
publisher.none.fl_str_mv Sociedade Brasileira de Econometria
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bitstream.url.fl_str_mv http://web.bndes.gov.br/bib/jspui/bitstream/1408/10614/1/Combining%20strategies%20for%20the%20estimation%20of%20treatment%20effects_Rafael%20Cayres_P_BD.pdf
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